3.1 Default

kable(
SummaryVars(data=sample.data,trt='Arm', subgroup='BEP', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"))
)

3.2 Compare to non-BEP instead of ITT

kable(
SummaryVars(data=sample.data,trt='Arm', subgroup='BEP', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"), compare.subgroup=TRUE)
)

3.3 Allow testing

kable(
SummaryVars(data=sample.data,trt='Arm', subgroup='BEP', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"), test.subgroup=TRUE)
)

## test.subgroup is TRUE but compare.subgroup is FALSE. Set compare.subgroup to TRUE

3.4 Reorder TRT/CTRL arm in display

kable(
SummaryVars(data=sample.data,trt='Arm', subgroup='BEP', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"), trt.order=c("TRT","CTRL"))
)

3.5 Combine trt arms

kable(
SummaryVars(data=sample.data, subgroup='BEP', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"))
)

3.6 Alternative BEP indicator

sample.data$BEP2 <- ifelse(sample.data$BEP==1,"Yes","No")
kable(
SummaryVars(data=sample.data,trt='Arm', subgroup='BEP2', var=c('Age','Sex'), 
       var.class=c("numeric","categorical"), subgroup.indicator="Yes")
)

4.1 Numeric biomarker variable. No clinical variables. Log transformation.

PlotProperty(data=input, biomarker.var="KRAS.exprs", biomarker.class="numeric", log2=TRUE)

4.2 No biomarker variable. Two numeric clinical variables. Log transformation for one varible.

PlotProperty(data=input, biomarker.var=NULL, biomarker.class=NULL,
             var=c("Weight","Age"), var.class=c("numeric", "numeric"),
             log2=c(TRUE, FALSE), par.param = list(mfrow=c(1,2), cex.axis=1.2))

4.3 Numeric biomarker variable. Two clinical categorical variables. Log transformation for numeric variable.

PlotProperty(data=input, biomarker.var="KRAS.exprs", biomarker.class="numeric",
             var=c("ECOG", "Country"), var.class=c("categorical", "categorical"),
             log2=TRUE, par.param = list(mfrow=c(3,2), cex.axis=1.2),
             show.clinical.uni=TRUE)

4.4 Numeric biomarker variable. Two clinical variables: one is categorocal, second is numeric. Log transformation for biomarker (numeric) variable.

PlotProperty(data=input, biomarker.var="KRAS.exprs", biomarker.class="numeric",
             var=c("Sex", "Age"), var.class=c("categorical", "numeric"),
             log2=c(TRUE, FALSE, FALSE), par.param = list(mfrow=c(3,2), cex.axis=1.4),
             show.clinical.uni=TRUE)

4.5 Categorical biomarker variable. Categorical clinical variable.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var="Country", var.class="categorical", 
             par.param = list(mfrow=c(2,2), cex.axis=1.2),
             show.clinical.uni=TRUE)

4.6 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(3,2), cex.axis=1.2),
             show.clinical.uni=TRUE)

4.7 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Don’t plot biomarker variable but plot clinical variables and association between biomarker and clinical variables.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(2,2), cex.axis=1.2), show.biomarker.uni = FALSE,
             show.clinical.uni=TRUE)

4.8 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Don’t plot clinical variables but plot biomarker variable and association between biomarker and clinical variables.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(2,2), cex.axis=1.2))

4.9 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Don’t plot association between variables but plot biomarker and clinical variables.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(2,2), cex.axis=1.2), show.association = FALSE,
             show.clinical.uni=TRUE)

4.10 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Plot only association between variables.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(1,2), cex.axis=1.2), 
             show.biomarker.uni = FALSE)

4.11 Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Plot only clinical variables.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(mfrow=c(1,2), cex.axis=1.1),
             show.biomarker.uni = FALSE, show.association = FALSE, 
             show.clinical.uni = TRUE)

## Categorial biomarker variable. Categorical and numeric clinical variables. No log transformation. Plot only biomarker variable.

PlotProperty(data=input, biomarker.var="KRAS.mutant", biomarker.class="categorical",
             var=c("Country", "Age"), var.class=c("categorical", "numeric"),
             par.param = list(cex.axis=1.2),
             show.association = FALSE)

5.1 Plot response of one population

PlotRspBar (input, outcome.var="Response",
            rsp.levels=c("CR", "PR","SD","NON CR/PD", "PD","NE"))

## entries with missing outcome.var are removed! 524 entries left

5.2 Plot response of one population, binarize outcome (classify classes to responder vs non responder)

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA))

## entries with missing outcome.var are removed! 524 entries left

5.3 Change color

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
            col=c("green","orange"))

## entries with missing outcome.var are removed! 524 entries left

5.4 Plot horizontally

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
            horiz=TRUE)

## entries with missing outcome.var are removed! 524 entries left

5.5 By arm

PlotRspBar (input, outcome.var="Response", 
            binary=FALSE,
            rsp.levels=c("CR", "PR","SD","NON CR/PD", "PD","NE"),
            trt="Arm")

## entries with missing outcome.var are removed! 524 entries left

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
            trt="Arm")

## entries with missing outcome.var are removed! 524 entries left

5.6 Compare full population vs BEP

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
            trt="Arm", compare.bep=TRUE,bep="BEP")

## entries with missing outcome.var are removed! 524 entries left

5.7 Compare subgroups

PlotRspBar (input, outcome.var="Response", 
            binary=TRUE,
            rsp.response = c("CR","PR"),
            rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
            trt="Arm", compare.var=TRUE,var="KRAS.mutant")

## entries with missing outcome.var are removed! 524 entries left

5.8 Plot count instead of percentage

PlotRspBar (input, outcome.var="Response", 
            binary=FALSE,
            rsp.levels=c("CR", "PR","SD","NON CR/PD", "PD","NE"),
            trt="Arm", 
            compare.var=TRUE,var="Sex", plot.count = TRUE)

## entries with missing outcome.var are removed! 524 entries left

6.1 Without subgroup

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS ITT"))

## [1] ""

6.2 Without subgroup, BEP

print(PlotKM(data=sample.data, bep="BEP",
             tte="PFS",cen="PFS.event", main="PFS BEP"))

## [1] ""

6.3 By TRT

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS ITT by treatment", trt="Arm"))

## [1] ""

6.4 By TRT, change color, line type

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS ITT by treatment", trt="Arm",
             col=c("orange","brown"),lty=c(1,2)))

## [1] ""

6.5 Mark median PFS, no grid

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS ITT by treatment", 
       trt="Arm", 
       plot.grid = FALSE, 
       plot.median=T))

## [1] ""

6.6 By KRAS.mutant only

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="OS BEP by treatment, by KRAS mutation", 
              var="KRAS.mutant"))

## [1] ""

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="OS BEP by treatment, by KRAS mutation", 
              var="KRAS.mutant", col=c("skyblue","darkgray")))

## [1] ""

6.7 By TRT and KRAS.mutant

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="OS BEP by treatment, by KRAS mutation", 
             trt="Arm", var="KRAS.mutant"))

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.8 By TRT and KRAS.mutant, change color and line type

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS BEP by treatment, by KRAS mutation", 
             trt="Arm", var="KRAS.mutant",
             col=c("orange","orange","brown","brown"),
             lty=c(3,1,3,1)))

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.9 By TRT and KRAS.expression (continuous biomarker, cut at median)

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS BEP by treatment, by KRAS expression", 
             trt="Arm", var="KRAS.exprs", var.class="numeric",
             percentile.cutoff=0.5,xlim=c(0,18))
             )

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.10 By TRT and KRAS.expression (continuous biomarker, cut at a numerical cutoff)

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS BEP by treatment, by KRAS expression", 
             trt="Arm", var="KRAS.exprs", var.class="numeric",
             numerical.cutoff=100)
             )

## numerical cutoff specified

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.11 By TRT and KRAS.expression (continuous biomarker, cut at a numerical cutoff). High group defined as “greater than”, instead of “greater than or equal to”

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS BEP by treatment, by KRAS expression", 
             trt="Arm", var="KRAS.exprs", var.class="numeric",
             numerical.cutoff=100, equal.in.high = F)
             )

## numerical cutoff specified

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.12 By TRT and KRAS.expression , more than 2 groups

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event",  
             main="PFS BEP by treatment, by KRAS expression", 
             trt="Arm", var="KRAS.exprs", var.class="numeric",
             numerical.cutoff=c(100,500), xlim=c(0, 20))
             )

## numerical cutoff specified

## entries who have NA in trt, var, or varlist are removed

## [1] ""

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             main="PFS BEP by treatment, by KRAS expression", 
             trt="Arm", var="KRAS.exprs", var.class="numeric",
             numerical.cutoff=c(100,500),
             col=c("green","green","green","brown","brown","brown"),
             xlim=c(0,20))
             )

## numerical cutoff specified

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.13 More flexibility, reorder subgroups

print(PlotKM(data=sample.data, tte="OS",cen="OS.event", 
             main="OS BEP by treatment, by KRAS mutation", 
             varlist=c("Arm","KRAS.mutant"), 
             varlist.levels=list(c("TRT","CTRL"),c("Wild Type","Mutant")),
             legend.loc="left"))

## 'varlist' is specified, trt and var parameters will be ignored

## entries who have NA in trt, var, or varlist are removed

## [1] ""

6.14 By TRT and KRAS.mutant, reorder and rename

print(PlotKM(data=sample.data, tte="OS",cen="OS.event", bep="BEP",
             main="OS BEP by treatment, by KRAS mutation", varlist=c("Arm","KRAS.mutant"), 
             varlist.levels=list(c("TRT","CTRL"),c("Wild Type","Mutant")),
             varlist.labels=list(c("trt","ctrl"),c("wt","mut")),
               plot.median=T,legend.loc="left"))

## 'varlist' is specified, trt and var parameters will be ignored

## [1] ""

6.15 Legend location

print(PlotKM(data=sample.data, tte="PFS",cen="PFS.event", 
             var=c("Arm","KRAS.mutant"), 
             bep="BEP", legend.loc=NULL, legend.x=5, legend.y=.8))

## more than one elements in 'var', trt parameter will be ignored

## 'varlist' is specified, trt and var parameters will be ignored

## [1] ""

7.1 Survival outcome, 2-arm, categorical variable

by default BEP will be defined as patients non-NA biomarker measurement

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical")

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                           
##  [1,] "Subgroup"               "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                    "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                       "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                    "CTRL"  "73 / 103"  "3.58" ""    
##  [5,] ""                       "TRT"   "118 / 223" "6.54" "0.5" 
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "26 / 34"   "3.22" ""    
##  [7,] ""                       "TRT"   "31 / 61"   "6.01" "0.48"
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "47 / 69"   "3.94" ""    
##  [9,] ""                       "TRT"   "87 / 162"  "6.57" "0.5" 
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.37 - 0.66" "0.0000028"  
##  [6,] ""            ""           
##  [7,] "0.28 - 0.8"  "0.0055"     
##  [8,] ""            ""           
##  [9,] "0.35 - 0.72" "0.00019"

7.2 Survival outcome, another format

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical",
                                  tabforest = TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                           
##  [1,] "Subgroup"               "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                    "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                       "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                    "CTRL"  "73 / 103"  "3.58" ""    
##  [5,] ""                       "TRT"   "118 / 223" "6.54" "0.5" 
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "26 / 34"   "3.22" ""    
##  [7,] ""                       "TRT"   "31 / 61"   "6.01" "0.48"
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "47 / 69"   "3.94" ""    
##  [9,] ""                       "TRT"   "87 / 162"  "6.57" "0.5" 
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.37 - 0.66" "0.0000028"  
##  [6,] ""            ""           
##  [7,] "0.28 - 0.8"  "0.0055"     
##  [8,] ""            ""           
##  [9,] "0.35 - 0.72" "0.00019"

7.3 Survival outcome, 2-arm, categorical variable, don’t show ITT, BEP, rename variable name in display

user can also define BEP column

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical",
                                  bep = 'BEP', 
                                  bep.indicator=1)

##                                code.v                           
##  [1,] "Subgroup"               "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                    "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                       "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                    "CTRL"  "59 / 81"   "3.58" ""    
##  [5,] ""                       "TRT"   "91 / 176"  "6.54" "0.49"
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "21 / 25"   "3.58" ""    
##  [7,] ""                       "TRT"   "28 / 55"   "5.72" "0.49"
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "38 / 56"   "3.45" ""    
##  [9,] ""                       "TRT"   "63 / 121"  "6.57" "0.5" 
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.36 - 0.69" "0.000031"   
##  [6,] ""            ""           
##  [7,] "0.28 - 0.86" "0.013"      
##  [8,] ""            ""           
##  [9,] "0.33 - 0.75" "0.00093"

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical", 
                                  var.name="KRAS mut",
                                  show.itt=FALSE, 
                                  show.bep=FALSE)

## Some NAs in var column, will define the non NA entries as BEP

##                            code.v                                        
## [1,] "Subgroup"            "Group" "Event/N"  "MST"  "HR"   "CI"         
## [2,] "KRAS mut(Mutant)"    "CTRL"  "26 / 34"  "3.22" ""     ""           
## [3,] ""                    "TRT"   "31 / 61"  "6.01" "0.48" "0.28 - 0.8" 
## [4,] "KRAS mut(Wild Type)" "CTRL"  "47 / 69"  "3.94" ""     ""           
## [5,] ""                    "TRT"   "87 / 162" "6.57" "0.5"  "0.35 - 0.72"
##               
## [1,] "raw P"  
## [2,] ""       
## [3,] "0.0055" 
## [4,] ""       
## [5,] "0.00019"

7.4 Survival outcome, 2-arm, continuous variable, greater than some percentage cutoffs

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                              #  cols=c("black","black","darkgreen","darkgreen","darkgreen"),
                                  numerical.cutoff=NULL,
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                   code.v                           
##  [1,] "Subgroup"                  "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                       "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                          "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                       "CTRL"  "102 / 152" "3.58" ""    
##  [5,] ""                          "TRT"   "167 / 309" "6.01" "0.56"
##  [6,] "KRAS exprs(>=25%, 23.26)"  "CTRL"  "73 / 111"  "3.38" ""    
##  [7,] ""                          "TRT"   "112 / 236" "6.93" "0.45"
##  [8,] "KRAS exprs(>=50%, 70.52)"  "CTRL"  "43 / 70"   "3.58" ""    
##  [9,] ""                          "TRT"   "73 / 162"  "7.16" "0.42"
## [10,] "KRAS exprs(>=75%, 177.29)" "CTRL"  "22 / 37"   "4.73" ""    
## [11,] ""                          "TRT"   "33 / 79"   "8.41" "0.38"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.43 - 0.71" "0.0000035"  
##  [6,] ""            ""           
##  [7,] "0.33 - 0.61" "0.00000015" 
##  [8,] ""            ""           
##  [9,] "0.29 - 0.62" "0.0000096"  
## [10,] ""            ""           
## [11,] "0.22 - 0.66" "0.00054"

7.5 Survival outcome, 2-arm, continuous variable, less than some percentage cutoffs

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=FALSE, less=TRUE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                                  bep = 'BEP', bep.indicator=1)

##                                  code.v                           
##  [1,] "Subgroup"                 "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                      "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                         "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                      "CTRL"  "59 / 81"   "3.58" ""    
##  [5,] ""                         "TRT"   "91 / 176"  "6.54" "0.49"
##  [6,] "KRAS exprs(<25%, 22.63)"  "CTRL"  "16 / 22"   "4.14" ""    
##  [7,] ""                         "TRT"   "30 / 42"   "5.45" "0.7" 
##  [8,] "KRAS exprs(<50%, 63.56)"  "CTRL"  "32 / 44"   "4.11" ""    
##  [9,] ""                         "TRT"   "52 / 84"   "5.72" "0.68"
## [10,] "KRAS exprs(<75%, 170.07)" "CTRL"  "44 / 61"   "3.91" ""    
## [11,] ""                         "TRT"   "75 / 131"  "5.59" "0.61"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.36 - 0.69" "0.000031"   
##  [6,] ""            ""           
##  [7,] "0.37 - 1.32" "0.27"       
##  [8,] ""            ""           
##  [9,] "0.43 - 1.06" "0.09"       
## [10,] ""            ""           
## [11,] "0.42 - 0.89" "0.0098"

7.6 Survival outcome, 2-arm, continuous variable, greater and less than some percentage cutoffs

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=TRUE, less=TRUE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                   code.v                           
##  [1,] "Subgroup"                  "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                       "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                          "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                       "CTRL"  "102 / 152" "3.58" ""    
##  [5,] ""                          "TRT"   "167 / 309" "6.01" "0.56"
##  [6,] "KRAS exprs(>=25%, 23.26)"  "CTRL"  "73 / 111"  "3.38" ""    
##  [7,] ""                          "TRT"   "112 / 236" "6.93" "0.45"
##  [8,] "KRAS exprs(>=50%, 70.52)"  "CTRL"  "43 / 70"   "3.58" ""    
##  [9,] ""                          "TRT"   "73 / 162"  "7.16" "0.42"
## [10,] "KRAS exprs(>=75%, 177.29)" "CTRL"  "22 / 37"   "4.73" ""    
## [11,] ""                          "TRT"   "33 / 79"   "8.41" "0.38"
## [12,] "KRAS exprs(<25%, 23.26)"   "CTRL"  "29 / 41"   "3.94" ""    
## [13,] ""                          "TRT"   "55 / 73"   "4.07" "1.04"
## [14,] "KRAS exprs(<50%, 70.52)"   "CTRL"  "59 / 82"   "3.91" ""    
## [15,] ""                          "TRT"   "94 / 147"  "5.42" "0.73"
## [16,] "KRAS exprs(<75%, 177.29)"  "CTRL"  "80 / 115"  "3.58" ""    
## [17,] ""                          "TRT"   "134 / 230" "5.59" "0.63"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.43 - 0.71" "0.0000035"  
##  [6,] ""            ""           
##  [7,] "0.33 - 0.61" "0.00000015" 
##  [8,] ""            ""           
##  [9,] "0.29 - 0.62" "0.0000096"  
## [10,] ""            ""           
## [11,] "0.22 - 0.66" "0.00054"    
## [12,] ""            ""           
## [13,] "0.66 - 1.64" "0.87"       
## [14,] ""            ""           
## [15,] "0.53 - 1.01" "0.06"       
## [16,] ""            ""           
## [17,] "0.48 - 0.84" "0.0013"

“Less” rows next to “Greater” rows:

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=TRUE, less=TRUE, greater.by.less = TRUE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                   code.v                           
##  [1,] "Subgroup"                  "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                       "CTRL"  "120 / 182" "3.25" ""    
##  [3,] ""                          "TRT"   "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                       "CTRL"  "102 / 152" "3.58" ""    
##  [5,] ""                          "TRT"   "167 / 309" "6.01" "0.56"
##  [6,] "KRAS exprs(>=25%, 23.26)"  "CTRL"  "73 / 111"  "3.38" ""    
##  [7,] ""                          "TRT"   "112 / 236" "6.93" "0.45"
##  [8,] "KRAS exprs(<25%, 23.26)"   "CTRL"  "29 / 41"   "3.94" ""    
##  [9,] ""                          "TRT"   "55 / 73"   "4.07" "1.04"
## [10,] "KRAS exprs(>=50%, 70.52)"  "CTRL"  "43 / 70"   "3.58" ""    
## [11,] ""                          "TRT"   "73 / 162"  "7.16" "0.42"
## [12,] "KRAS exprs(<50%, 70.52)"   "CTRL"  "59 / 82"   "3.91" ""    
## [13,] ""                          "TRT"   "94 / 147"  "5.42" "0.73"
## [14,] "KRAS exprs(>=75%, 177.29)" "CTRL"  "22 / 37"   "4.73" ""    
## [15,] ""                          "TRT"   "33 / 79"   "8.41" "0.38"
## [16,] "KRAS exprs(<75%, 177.29)"  "CTRL"  "80 / 115"  "3.58" ""    
## [17,] ""                          "TRT"   "134 / 230" "5.59" "0.63"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.43 - 0.71" "0.0000035"  
##  [6,] ""            ""           
##  [7,] "0.33 - 0.61" "0.00000015" 
##  [8,] ""            ""           
##  [9,] "0.66 - 1.64" "0.87"       
## [10,] ""            ""           
## [11,] "0.29 - 0.62" "0.0000096"  
## [12,] ""            ""           
## [13,] "0.53 - 1.01" "0.06"       
## [14,] ""            ""           
## [15,] "0.22 - 0.66" "0.00054"    
## [16,] ""            ""           
## [17,] "0.48 - 0.84" "0.0013"

7.7 Survival outcome, 2-arm, continuous variable, within cutoff bin

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=FALSE, less=FALSE,
                                  within.bin=TRUE,
                                  show.itt=TRUE, show.bep=TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                             code.v                    
##  [1,] "Subgroup"                            "Group" "Event/N"   "MST" 
##  [2,] "All"                                 "CTRL"  "120 / 182" "3.25"
##  [3,] ""                                    "TRT"   "202 / 368" "5.82"
##  [4,] "BEP"                                 "CTRL"  "102 / 152" "3.58"
##  [5,] ""                                    "TRT"   "167 / 309" "6.01"
##  [6,] "KRAS exprs[0-25%, 1.41-23.26)"       "CTRL"  "29 / 41"   "3.94"
##  [7,] ""                                    "TRT"   "55 / 73"   "4.07"
##  [8,] "KRAS exprs[25-50%, 23.26-70.52)"     "CTRL"  "30 / 41"   "3.15"
##  [9,] ""                                    "TRT"   "39 / 74"   "6.67"
## [10,] "KRAS exprs[50-75%, 70.52-177.29)"    "CTRL"  "21 / 33"   "3.22"
## [11,] ""                                    "TRT"   "40 / 83"   "5.82"
## [12,] "KRAS exprs[75-100%, 177.29-9541.51]" "CTRL"  "22 / 37"   "4.73"
## [13,] ""                                    "TRT"   "33 / 79"   "8.41"
##                                         
##  [1,] "HR"   "CI"          "raw P"      
##  [2,] ""     ""            ""           
##  [3,] "0.54" "0.43 - 0.67" "0.000000078"
##  [4,] ""     ""            ""           
##  [5,] "0.56" "0.43 - 0.71" "0.0000035"  
##  [6,] ""     ""            ""           
##  [7,] "1.04" "0.66 - 1.64" "0.87"       
##  [8,] ""     ""            ""           
##  [9,] "0.51" "0.31 - 0.82" "0.0057"     
## [10,] ""     ""            ""           
## [11,] "0.46" "0.27 - 0.79" "0.0049"     
## [12,] ""     ""            ""           
## [13,] "0.38" "0.22 - 0.66" "0.00054"

7.8 Survival outcome, 2-arm, continuous variable, greater than some numerical cutoffs

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=NULL,
                                  numerical.cutoff=c(50,100),
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                           code.v                                         
##  [1,] "Subgroup"          "Group" "Event/N"   "MST"  "HR"   "CI"         
##  [2,] "All"               "CTRL"  "120 / 182" "3.25" ""     ""           
##  [3,] ""                  "TRT"   "202 / 368" "5.82" "0.54" "0.43 - 0.67"
##  [4,] "BEP"               "CTRL"  "102 / 152" "3.58" ""     ""           
##  [5,] ""                  "TRT"   "167 / 309" "6.01" "0.56" "0.43 - 0.71"
##  [6,] "KRAS exprs(>=50)"  "CTRL"  "54 / 85"   "3.45" ""     ""           
##  [7,] ""                  "TRT"   "85 / 189"  "7.16" "0.42" "0.3 - 0.6"  
##  [8,] "KRAS exprs(>=100)" "CTRL"  "32 / 53"   "3.45" ""     ""           
##  [9,] ""                  "TRT"   "56 / 125"  "7.82" "0.42" "0.27 - 0.65"
##                    
##  [1,] "raw P"      
##  [2,] ""           
##  [3,] "0.000000078"
##  [4,] ""           
##  [5,] "0.0000035"  
##  [6,] ""           
##  [7,] "0.0000012"  
##  [8,] ""           
##  [9,] "0.00011"

7.9 Survival outcome, 2-arm, continuous variable, adjust for covariates

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=NULL,
                                  numerical.cutoff=c(50,100),
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                                  bep = 'BEP', bep.indicator=1, covariate="Age")

##                           code.v                                         
##  [1,] "Subgroup"          "Group" "Event/N"   "MST"  "HR"   "CI"         
##  [2,] "All"               "CTRL"  "120 / 182" "3.25" ""     ""           
##  [3,] ""                  "TRT"   "202 / 368" "5.82" "0.53" "0.42 - 0.67"
##  [4,] "BEP"               "CTRL"  "59 / 81"   "3.58" ""     ""           
##  [5,] ""                  "TRT"   "91 / 176"  "6.54" "0.5"  "0.36 - 0.69"
##  [6,] "KRAS exprs(>=50)"  "CTRL"  "31 / 42"   "3.38" ""     ""           
##  [7,] ""                  "TRT"   "45 / 104"  "8.41" "0.38" "0.24 - 0.61"
##  [8,] "KRAS exprs(>=100)" "CTRL"  "20 / 28"   "3.45" ""     ""           
##  [9,] ""                  "TRT"   "28 / 71"   "8.51" "0.34" "0.19 - 0.61"
##                   
##  [1,] "raw P"     
##  [2,] ""          
##  [3,] "0.00000005"
##  [4,] ""          
##  [5,] "0.000031"  
##  [6,] ""          
##  [7,] "0.00005"   
##  [8,] ""          
##  [9,] "0.00034"

7.10 Survival outcome, 2-arm, continous variable, stratification

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=NULL,
                                  numerical.cutoff=c(50,100),
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                                  bep = 'BEP', bep.indicator=1, strata="Sex")

##                           code.v                                         
##  [1,] "Subgroup"          "Group" "Event/N"   "MST"  "HR"   "CI"         
##  [2,] "All"               "CTRL"  "120 / 182" "3.25" ""     ""           
##  [3,] ""                  "TRT"   "202 / 368" "5.82" "0.54" "0.43 - 0.67"
##  [4,] "BEP"               "CTRL"  "59 / 81"   "3.58" ""     ""           
##  [5,] ""                  "TRT"   "91 / 176"  "6.54" "0.5"  "0.36 - 0.69"
##  [6,] "KRAS exprs(>=50)"  "CTRL"  "31 / 42"   "3.38" ""     ""           
##  [7,] ""                  "TRT"   "45 / 104"  "8.41" "0.38" "0.24 - 0.61"
##  [8,] "KRAS exprs(>=100)" "CTRL"  "20 / 28"   "3.45" ""     ""           
##  [9,] ""                  "TRT"   "28 / 71"   "8.51" "0.34" "0.19 - 0.63"
##                    
##  [1,] "raw P"      
##  [2,] ""           
##  [3,] "0.000000084"
##  [4,] ""           
##  [5,] "0.000038"   
##  [6,] ""           
##  [7,] "0.000067"   
##  [8,] ""           
##  [9,] "0.00046"

7.11 Survival outcome, 2-arm, continuous variable, adjust for covariates and stratified by strata

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=NULL,
                                  numerical.cutoff=c(50,100),
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                                  bep = 'BEP', bep.indicator=1,covariate="Age",strata="Sex")

##                           code.v                                         
##  [1,] "Subgroup"          "Group" "Event/N"   "MST"  "HR"   "CI"         
##  [2,] "All"               "CTRL"  "120 / 182" "3.25" ""     ""           
##  [3,] ""                  "TRT"   "202 / 368" "5.82" "0.53" "0.42 - 0.67"
##  [4,] "BEP"               "CTRL"  "59 / 81"   "3.58" ""     ""           
##  [5,] ""                  "TRT"   "91 / 176"  "6.54" "0.5"  "0.36 - 0.69"
##  [6,] "KRAS exprs(>=50)"  "CTRL"  "31 / 42"   "3.38" ""     ""           
##  [7,] ""                  "TRT"   "45 / 104"  "8.41" "0.39" "0.24 - 0.62"
##  [8,] "KRAS exprs(>=100)" "CTRL"  "20 / 28"   "3.45" ""     ""           
##  [9,] ""                  "TRT"   "28 / 71"   "8.51" "0.34" "0.19 - 0.62"
##                    
##  [1,] "raw P"      
##  [2,] ""           
##  [3,] "0.000000053"
##  [4,] ""           
##  [5,] "0.000035"   
##  [6,] ""           
##  [7,] "0.000078"   
##  [8,] ""           
##  [9,] "0.00039"

7.12 Survival outcome, 1arm

within.bin, show.itt, show.bep will be ignored

PlotTabForestBiomarker(data=subset(input, Arm=="TRT"),
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt=NULL,
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=NULL,
                                  numerical.cutoff=c(50,100),
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,covariate="Age",strata="Sex")

## Some NAs in var column, will define the non NA entries as BEP

## only 1 arm; show.itt is set to FALSE

## only 1 arm; show.bep is set to FALSE

##                          code.v                                           
## [1,] "Subgroup"          "Group"   "Event/N"   "MST"  "HR"   "CI"         
## [2,] "KRAS exprs(>=50)"  "Less"    "82 / 120"  "5.39" ""     ""           
## [3,] ""                  "Greater" "85 / 189"  "7.16" "0.54" "0.4 - 0.74" 
## [4,] "KRAS exprs(>=100)" "Less"    "111 / 184" "5.59" ""     ""           
## [5,] ""                  "Greater" "56 / 125"  "7.82" "0.57" "0.41 - 0.78"
##                
## [1,] "raw P"   
## [2,] ""        
## [3,] "0.000095"
## [4,] ""        
## [5,] "0.00062"

7.13 Survival outcome, 2arm, flip TRT/CTRL order (calculate ctrl over trt HR)

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                                  covariate=NULL, #Sex
                                  strata=NULL, #Age
                                  placebo.code='TRT',
                                  active.code='CTRL')

## Some NAs in var column, will define the non NA entries as BEP

##                                   code.v                           
##  [1,] "Subgroup"                  "Group" "Event/N"   "MST"  "HR"  
##  [2,] "All"                       "TRT"   "202 / 368" "5.82" ""    
##  [3,] ""                          "CTRL"  "120 / 182" "3.25" "1.87"
##  [4,] "BEP"                       "TRT"   "167 / 309" "6.01" ""    
##  [5,] ""                          "CTRL"  "102 / 152" "3.58" "1.8" 
##  [6,] "KRAS exprs(>=25%, 23.26)"  "TRT"   "112 / 236" "6.93" ""    
##  [7,] ""                          "CTRL"  "73 / 111"  "3.38" "2.23"
##  [8,] "KRAS exprs(>=50%, 70.52)"  "TRT"   "73 / 162"  "7.16" ""    
##  [9,] ""                          "CTRL"  "43 / 70"   "3.58" "2.37"
## [10,] "KRAS exprs(>=75%, 177.29)" "TRT"   "33 / 79"   "8.41" ""    
## [11,] ""                          "CTRL"  "22 / 37"   "4.73" "2.64"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "1.49 - 2.34" "0.000000078"
##  [4,] ""            ""           
##  [5,] "1.4 - 2.3"   "0.0000035"  
##  [6,] ""            ""           
##  [7,] "1.65 - 3"    "0.00000015" 
##  [8,] ""            ""           
##  [9,] "1.62 - 3.48" "0.0000096"  
## [10,] ""            ""           
## [11,] "1.52 - 4.57" "0.00054"

7.14 Survival outcome, 2-arm, continuous variable, greater than some percentage cutoffs, show across-arm and within-arm results in the same plot

PlotTabForestBiomarker(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var="KRAS.exprs", 
                                  var.class="numeric", var.name="KRAS exprs",
                                  percentile.cutoff=c(.25,.5,.75), 
                                  numerical.cutoff=NULL,
                                  greater=TRUE, less=FALSE,
                                  within.bin=FALSE,
                                  show.itt=TRUE, show.bep=TRUE,
                       across.and.within = TRUE)

## Some NAs in var column, will define the non NA entries as BEP

##                                        code.v                             
##  [1,] "Subgroup"                       "Group"   "Event/N"   "MST"  "HR"  
##  [2,] "All"                            "CTRL"    "120 / 182" "3.25" ""    
##  [3,] ""                               "TRT"     "202 / 368" "5.82" "0.54"
##  [4,] "BEP"                            "CTRL"    "102 / 152" "3.58" ""    
##  [5,] ""                               "TRT"     "167 / 309" "6.01" "0.56"
##  [6,] "KRAS exprs(>=25%, 23.26)"       "CTRL"    "73 / 111"  "3.38" ""    
##  [7,] ""                               "TRT"     "112 / 236" "6.93" "0.45"
##  [8,] "KRAS exprs(>=50%, 70.52)"       "CTRL"    "43 / 70"   "3.58" ""    
##  [9,] ""                               "TRT"     "73 / 162"  "7.16" "0.42"
## [10,] "KRAS exprs(>=75%, 177.29)"      "CTRL"    "22 / 37"   "4.73" ""    
## [11,] ""                               "TRT"     "33 / 79"   "8.41" "0.38"
## [12,] "CTRL:KRAS exprs(>=25%, 23.26)"  "Less"    "29 / 41"   "3.94" ""    
## [13,] ""                               "Greater" "73 / 111"  "3.38" "1.09"
## [14,] "CTRL:KRAS exprs(>=50%, 70.52)"  "Less"    "59 / 82"   "3.91" ""    
## [15,] ""                               "Greater" "43 / 70"   "3.58" "1"   
## [16,] "CTRL:KRAS exprs(>=75%, 177.29)" "Less"    "80 / 115"  "3.58" ""    
## [17,] ""                               "Greater" "22 / 37"   "4.73" "0.85"
## [18,] "TRT:KRAS exprs(>=25%, 23.26)"   "Less"    "55 / 73"   "4.07" ""    
## [19,] ""                               "Greater" "112 / 236" "6.93" "0.45"
## [20,] "TRT:KRAS exprs(>=50%, 70.52)"   "Less"    "94 / 147"  "5.42" ""    
## [21,] ""                               "Greater" "73 / 162"  "7.16" "0.57"
## [22,] "TRT:KRAS exprs(>=75%, 177.29)"  "Less"    "134 / 230" "5.59" ""    
## [23,] ""                               "Greater" "33 / 79"   "8.41" "0.51"
##                                  
##  [1,] "CI"          "raw P"      
##  [2,] ""            ""           
##  [3,] "0.43 - 0.67" "0.000000078"
##  [4,] ""            ""           
##  [5,] "0.43 - 0.71" "0.0000035"  
##  [6,] ""            ""           
##  [7,] "0.33 - 0.61" "0.00000015" 
##  [8,] ""            ""           
##  [9,] "0.29 - 0.62" "0.0000096"  
## [10,] ""            ""           
## [11,] "0.22 - 0.66" "0.00054"    
## [12,] ""            ""           
## [13,] "0.7 - 1.68"  "0.71"       
## [14,] ""            ""           
## [15,] "0.67 - 1.48" "1"          
## [16,] ""            ""           
## [17,] "0.53 - 1.37" "0.5"        
## [18,] ""            ""           
## [19,] "0.33 - 0.63" "0.000002"   
## [20,] ""            ""           
## [21,] "0.42 - 0.78" "0.00043"    
## [22,] ""            ""           
## [23,] "0.35 - 0.75" "0.00061"

7.15 Response outcome, 2-arm, categorical variable

by default BEP will be defined as patients non-NA biomarker measurement

PlotTabForestBiomarker(data=input,
                                  outcome.class="binary",
                                  outcome.var=c("Response"),
                                  rsp.cat = TRUE,
                                  rsp.response = c("CR","PR"),
                                  rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical")

## Covariate adjustment and stratification are not supported for binary outcome

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                                 
##  [1,] "Subgroup"               "Group" "nRsp/N"   "Rsp Rate" "deltaRR"
##  [2,] "All"                    "CTRL"  "14 / 182" "0.08"     ""       
##  [3,] ""                       "TRT"   "96 / 368" "0.26"     "0.18"   
##  [4,] "BEP"                    "CTRL"  "11 / 103" "0.11"     ""       
##  [5,] ""                       "TRT"   "54 / 223" "0.24"     "0.14"   
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "5 / 34"   "0.15"     ""       
##  [7,] ""                       "TRT"   "18 / 61"  "0.3"      "0.15"   
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "6 / 69"   "0.09"     ""       
##  [9,] ""                       "TRT"   "36 / 162" "0.22"     "0.14"   
##                                 
##  [1,] "CI"           "raw P"    
##  [2,] ""             ""         
##  [3,] "0.12 - 0.25"  "0.0000007"
##  [4,] ""             ""         
##  [5,] "0.05 - 0.22"  "0.007"    
##  [6,] ""             ""         
##  [7,] "-0.04 - 0.34" "0.17"     
##  [8,] ""             ""         
##  [9,] "0.03 - 0.24"  "0.024"

7.16 Response outcome, another format

PlotTabForestBiomarker(data=input,                                  
                                  outcome.class="binary",
                                  outcome.var=c("Response"),
                                  rsp.cat = TRUE,
                                  rsp.response = c("CR","PR"),
                                  rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical",
                                  tabforest = TRUE)

## Covariate adjustment and stratification are not supported for binary outcome

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                                 
##  [1,] "Subgroup"               "Group" "nRsp/N"   "Rsp Rate" "deltaRR"
##  [2,] "All"                    "CTRL"  "14 / 182" "0.08"     ""       
##  [3,] ""                       "TRT"   "96 / 368" "0.26"     "0.18"   
##  [4,] "BEP"                    "CTRL"  "11 / 103" "0.11"     ""       
##  [5,] ""                       "TRT"   "54 / 223" "0.24"     "0.14"   
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "5 / 34"   "0.15"     ""       
##  [7,] ""                       "TRT"   "18 / 61"  "0.3"      "0.15"   
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "6 / 69"   "0.09"     ""       
##  [9,] ""                       "TRT"   "36 / 162" "0.22"     "0.14"   
##                                 
##  [1,] "CI"           "raw P"    
##  [2,] ""             ""         
##  [3,] "0.12 - 0.25"  "0.0000007"
##  [4,] ""             ""         
##  [5,] "0.05 - 0.22"  "0.007"    
##  [6,] ""             ""         
##  [7,] "-0.04 - 0.34" "0.17"     
##  [8,] ""             ""         
##  [9,] "0.03 - 0.24"  "0.024"

7.17 Continuous outcome, 2-arm, categorical variable

PlotTabForestBiomarker(data=input,
                                  outcome.class="continuous",
                                  outcome.var=c("Lab_ontrt"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical")

## Stratification is not supported for continuous outcome

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                               
##  [1,] "Subgroup"               "Group" "Mean"  "delta" "CI"         
##  [2,] "All"                    "CTRL"  "19.22" ""      ""           
##  [3,] ""                       "TRT"   "22.17" "2.95"  "1.64 - 4.26"
##  [4,] "BEP"                    "CTRL"  "19.67" ""      ""           
##  [5,] ""                       "TRT"   "22.42" "2.75"  "1.06 - 4.44"
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "19.54" ""      ""           
##  [7,] ""                       "TRT"   "23.22" "3.68"  "0.56 - 6.8" 
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "19.73" ""      ""           
##  [9,] ""                       "TRT"   "22.11" "2.38"  "0.36 - 4.41"
##                 
##  [1,] "raw P"   
##  [2,] ""        
##  [3,] "0.000012"
##  [4,] ""        
##  [5,] "0.0015"  
##  [6,] ""        
##  [7,] "0.021"   
##  [8,] ""        
##  [9,] "0.021"

PlotTabForestBiomarker(data=input,
                                  outcome.class="continuous",
                                  outcome.var=c("Lab_ontrt"),
                                  trt="Arm",
                                  var="KRAS.mutant", 
                                  var.class="categorical",tabforest=T)

## Stratification is not supported for continuous outcome

## Some NAs in var column, will define the non NA entries as BEP

##                                code.v                               
##  [1,] "Subgroup"               "Group" "Mean"  "delta" "CI"         
##  [2,] "All"                    "CTRL"  "19.22" ""      ""           
##  [3,] ""                       "TRT"   "22.17" "2.95"  "1.64 - 4.26"
##  [4,] "BEP"                    "CTRL"  "19.67" ""      ""           
##  [5,] ""                       "TRT"   "22.42" "2.75"  "1.06 - 4.44"
##  [6,] "KRAS.mutant(Mutant)"    "CTRL"  "19.54" ""      ""           
##  [7,] ""                       "TRT"   "23.22" "3.68"  "0.56 - 6.8" 
##  [8,] "KRAS.mutant(Wild Type)" "CTRL"  "19.73" ""      ""           
##  [9,] ""                       "TRT"   "22.11" "2.38"  "0.36 - 4.41"
##                 
##  [1,] "raw P"   
##  [2,] ""        
##  [3,] "0.000012"
##  [4,] ""        
##  [5,] "0.0015"  
##  [6,] ""        
##  [7,] "0.021"   
##  [8,] ""        
##  [9,] "0.021"

8.1 2-arm, compare BEP vs ITT

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1,
                   compare.bep.itt=TRUE
                   )

8.2 2-arm, compare subgroups defined by biomarker

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant"
                   )

8.3 2-arm, compare subgroups defined by biomarker, also show ITT

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant", show.itt=TRUE
                   )

8.4 2-arm, compare subgroups defined by biomarker, also show BEP

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant", show.itt=TRUE, show.bep=TRUE
                   )

If bep column is not defined, the program will take the non NA entries in subgroups column as BEP

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"), compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant", show.itt=TRUE, show.bep=TRUE
                   )

## show.bep is TRUE but bep is not specified, will define the non NA entries in subgroup column as BEP

8.5 1-arm, compare ITT vs BEP

PlotTabForestMulti(data=subset(input,Arm=="TRT"),
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt=NULL,
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=TRUE
                   )

8.6 2-arm, compare subgroups defined by biomarker, multiple cutoffs for continuous biomarker

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm", percentile.cutoff=c(.33,.66),
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant"
                   )

8.7 2-arm, compare subgroups defined by biomarker, multiple cutoffs for continuous biomarker, calculate “< cutoff”

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm", percentile.cutoff=c(.33,.66),less=TRUE,greater=FALSE,
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant"
                   )

8.8 2-arm, compare subgroups defined by biomarker, multiple cutoffs for continuous biomarker, calculate within bin statistics

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm", percentile.cutoff=c(.33,.66),within.bin=TRUE,
                                  var=c("Sex","Age"),
                   bep="BEP",bep.indicator=1, compare.bep.itt=FALSE,
                   compare.subgroup=TRUE, subgroup="KRAS.mutant"
                   )

## within.bin is TRUE, greater and less will be ignored
## within.bin is TRUE, greater and less will be ignored
## within.bin is TRUE, greater and less will be ignored
## within.bin is TRUE, greater and less will be ignored

8.9 Basic forest plot without comparison

PlotTabForestMulti(data=input,
                                  outcome.class="survival",
                                  outcome.var=c("PFS","PFS.event"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   compare.bep.itt=FALSE, compare.subgroup=FALSE,itt.name=""
                   )

8.10 Response outcome

PlotTabForestMulti(data=input,
                                                                   outcome.class="binary",
                                  outcome.var=c("Response"),
                                  rsp.cat = TRUE,
                                  rsp.response = c("CR","PR"),
                                  rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   compare.bep.itt=FALSE, compare.subgroup=FALSE,itt.name=""
                   )

## Covariate adjustment and stratification are not supported for binary outcome
## Covariate adjustment and stratification are not supported for binary outcome

PlotTabForestMulti(data=input,
                                                                   outcome.class="binary",
                                  outcome.var=c("Response"),
                                  rsp.cat = TRUE,
                                  rsp.response = c("CR","PR"),
                                  rsp.nonresponse = c("SD", "PD","NON CR/PD","NE",NA),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   compare.bep.itt=FALSE, compare.subgroup=FALSE,itt.name="", tabforest=T
                   )

## Covariate adjustment and stratification are not supported for binary outcome
## Covariate adjustment and stratification are not supported for binary outcome

## Warning in log(clip): NaNs produced

8.11 Continuous endpoint

PlotTabForestMulti(data=input,
                               outcome.class="continuous",
                                  outcome.var=c("Lab_ontrt"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   compare.bep.itt=FALSE, compare.subgroup=FALSE,itt.name=""
                   )

## Stratification is not supported for continuous outcome
## Stratification is not supported for continuous outcome

PlotTabForestMulti(data=input,
                 outcome.class="continuous",
                                  outcome.var=c("Lab_ontrt"),
                                  trt="Arm",
                                  var=c("Sex","Age"),
                   compare.bep.itt=FALSE, compare.subgroup=FALSE,itt.name="", tabforest=T
                   )

## Stratification is not supported for continuous outcome
## Stratification is not supported for continuous outcome

9.1 Survival outcome

PlotSTEPP(data = input,
          outcome.var = c("PFS", "PFS.event"),
          outcome.class = "survival",
          trt = "Arm",
          var = "KRAS.exprs",
          placebo.code = "CTRL",
          active.code = "TRT",
          csv.name = NULL,
          pdf.name = NULL
) 

## some NA in var column, will ignore NA entries

##       Window Center Hazard Ratio  CI Lower  CI Upper BM Lower BM Upper
##  [1,]         0.125    1.0382905 0.6576013 1.6393629     1.42    23.26
##  [2,]         0.175    0.9116623 0.5769829 1.4404727     5.98    32.00
##  [3,]         0.225    0.8168277 0.5196594 1.2839325     9.51    42.81
##  [4,]         0.275    0.7091655 0.4465512 1.1262218    14.03    48.84
##  [5,]         0.325    0.6512376 0.4040444 1.0496628    19.70    59.30
##  [6,]         0.375    0.5074886 0.3136041 0.8212413    23.26    70.52
##  [7,]         0.425    0.4542622 0.2700666 0.7640860    32.00    77.71
##  [8,]         0.475    0.4208886 0.2493019 0.7105732    42.81    95.01
##  [9,]         0.525    0.5477689 0.3262293 0.9197544    48.84   121.94
## [10,]         0.575    0.4430307 0.2645633 0.7418874    59.30   151.17
## [11,]         0.625    0.4636518 0.2714610 0.7919111    70.52   177.29
## [12,]         0.675    0.4121338 0.2414306 0.7035327    77.71   213.78
## [13,]         0.725    0.3650580 0.2102587 0.6338257    95.01   259.57
## [14,]         0.775    0.3304610 0.1856466 0.5882385   121.94   317.37
## [15,]         0.825    0.3903051 0.2270411 0.6709713   151.17   467.88
## [16,]         0.875    0.3912302 0.2241152 0.6829571   177.29  9541.50
##       BM Center Window Left Window Right   N Events
##  [1,]    11.010        0.00         0.25 114     84
##  [2,]    16.450        0.05         0.30 115     84
##  [3,]    21.860        0.10         0.35 115     85
##  [4,]    27.860        0.15         0.40 115     78
##  [5,]    36.250        0.20         0.45 115     73
##  [6,]    45.250        0.25         0.50 115     69
##  [7,]    53.265        0.30         0.55 114     59
##  [8,]    64.450        0.35         0.60 115     59
##  [9,]    73.010        0.40         0.65 115     63
## [10,]    88.030        0.45         0.70 115     62
## [11,]   101.480        0.50         0.75 116     61
## [12,]   133.900        0.55         0.80 116     61
## [13,]   165.420        0.60         0.85 115     55
## [14,]   195.360        0.65         0.90 115     51
## [15,]   238.860        0.70         0.95 115     56
## [16,]   292.040        0.75         1.00 115     54

9.2 Continuous outcome

PlotSTEPP(data = input,
          outcome.var = "Baseline.SLD",
          outcome.class = "continuous",
          trt = "Arm",
          var = "KRAS.exprs",
          covariate= "Sex",
          placebo.code = "CTRL",
          active.code = "TRT",
          csv.name = NULL,
          pdf.name = NULL
)

## some NA in var column, will ignore NA entries

##       Window Center Mean Difference    CI Lower  CI Upper BM Lower
##  [1,]         0.125      0.18035575 -0.24729997 0.6080115     1.42
##  [2,]         0.175      0.34651479 -0.06475305 0.7577826     5.98
##  [3,]         0.225      0.44278821  0.03092580 0.8546506     9.51
##  [4,]         0.275      0.33749063 -0.07236992 0.7473512    14.03
##  [5,]         0.325      0.10572728 -0.30412158 0.5155761    19.70
##  [6,]         0.375      0.15866042 -0.24724256 0.5645634    23.26
##  [7,]         0.425     -0.03630432 -0.45831952 0.3857109    32.00
##  [8,]         0.475     -0.10286329 -0.49436652 0.2886399    42.81
##  [9,]         0.525     -0.14583073 -0.54953031 0.2578689    48.84
## [10,]         0.575      0.02704796 -0.36419706 0.4182930    59.30
## [11,]         0.625     -0.16994763 -0.56071364 0.2208184    70.52
## [12,]         0.675     -0.03456171 -0.41907553 0.3499521    77.71
## [13,]         0.725     -0.09734050 -0.50684711 0.3121661    95.01
## [14,]         0.775      0.06465414 -0.34072771 0.4700360   121.94
## [15,]         0.825     -0.02383926 -0.40821143 0.3605329   151.17
## [16,]         0.875     -0.05926090 -0.45420790 0.3356861   177.29
##       BM Upper BM Center Window Left Window Right   N
##  [1,]    23.26    11.010        0.00         0.25 114
##  [2,]    32.00    16.450        0.05         0.30 115
##  [3,]    42.81    21.860        0.10         0.35 115
##  [4,]    48.84    27.860        0.15         0.40 115
##  [5,]    59.30    36.250        0.20         0.45 115
##  [6,]    70.52    45.250        0.25         0.50 115
##  [7,]    77.71    53.265        0.30         0.55 114
##  [8,]    95.01    64.450        0.35         0.60 115
##  [9,]   121.94    73.010        0.40         0.65 115
## [10,]   151.17    88.030        0.45         0.70 115
## [11,]   177.29   101.480        0.50         0.75 116
## [12,]   213.78   133.900        0.55         0.80 116
## [13,]   259.57   165.420        0.60         0.85 115
## [14,]   317.37   195.360        0.65         0.90 115
## [15,]   467.88   238.860        0.70         0.95 115
## [16,]  9541.50   292.040        0.75         1.00 115

9.3 Binary outcome variable

PlotSTEPP(data = input,
          outcome.var = "ECOG",
          outcome.class = "binary",
          trt = "Arm",
          var = "KRAS.exprs",
          placebo.code = "CTRL",
          active.code = "TRT",
          csv.name = NULL,
          pdf.name = NULL
)

## some NA in var column, will ignore NA entries

##       Window Center Proportion Difference    CI Lower     CI Upper
##  [1,]         0.125           0.096558637 -0.10936788 0.3024851571
##  [2,]         0.175           0.178408350 -0.02235039 0.3791670900
##  [3,]         0.225           0.147000659 -0.05041839 0.3444197101
##  [4,]         0.275           0.095583388 -0.10218602 0.2933527961
##  [5,]         0.325           0.119973632 -0.07934753 0.3192947989
##  [6,]         0.375           0.114700066 -0.08986157 0.3192616985
##  [7,]         0.425           0.078947368 -0.13057600 0.2884707407
##  [8,]         0.475          -0.010179010 -0.21308461 0.1927265895
##  [9,]         0.525          -0.001808318 -0.20059338 0.1969767445
## [10,]         0.575          -0.046292948 -0.26043294 0.1678470400
## [11,]         0.625          -0.141906874 -0.34878874 0.0649749952
## [12,]         0.675          -0.196428571 -0.39328032 0.0004231801
## [13,]         0.725          -0.187500000 -0.37820558 0.0032055783
## [14,]         0.775          -0.159638554 -0.36486679 0.0455896813
## [15,]         0.825          -0.074191280 -0.27905945 0.1306768944
## [16,]         0.875           0.004219409 -0.18560507 0.1940438921
##       BM Lower BM Upper BM Center Window Left Window Right   N
##  [1,]     1.42    23.26    11.010        0.00         0.25 114
##  [2,]     5.98    32.00    16.450        0.05         0.30 115
##  [3,]     9.51    42.81    21.860        0.10         0.35 115
##  [4,]    14.03    48.84    27.860        0.15         0.40 115
##  [5,]    19.70    59.30    36.250        0.20         0.45 115
##  [6,]    23.26    70.52    45.250        0.25         0.50 115
##  [7,]    32.00    77.71    53.265        0.30         0.55 114
##  [8,]    42.81    95.01    64.450        0.35         0.60 115
##  [9,]    48.84   121.94    73.010        0.40         0.65 115
## [10,]    59.30   151.17    88.030        0.45         0.70 115
## [11,]    70.52   177.29   101.480        0.50         0.75 116
## [12,]    77.71   213.78   133.900        0.55         0.80 116
## [13,]    95.01   259.57   165.420        0.60         0.85 115
## [14,]   121.94   317.37   195.360        0.65         0.90 115
## [15,]   151.17   467.88   238.860        0.70         0.95 115
## [16,]   177.29  9541.50   292.040        0.75         1.00 115

10.1 single covariate

CoxTab(data=sample.data, tte="OS", cens="OS.event",bep='BEP', var='Sex' )

##           HR     CI.low CI.high p-value n.trt n.ref
## Sex (M/F) "0.85" "0.42" "1.72"  "0.65"  "128" "129"

10.2 multiple covariates, ITT

Additive model will be fitted.

kable(
CoxTab(data=sample.data,tte="OS", cens="OS.event",  var=c('Sex',"Country","Age"), 
       var.class=c("categorical","categorical","numeric"))
)

Simplify: automatically learn var.class from column class:

kable(
CoxTab(data=sample.data,tte="OS", cens="OS.event",  var=c('Sex',"Country","Age"))
)

10.3 BEP

kable(
CoxTab(data=sample.data,tte="OS", cens="OS.event",  var=c('Sex',"Country","Age"), 
       var.class=c("categorical","categorical","numeric"), bep="BEP")
)

10.4 Reorder Sex: M as reference

kable(
CoxTab(data=sample.data,tte="OS", cens="OS.event",  var=c('Sex',"Country","Age"), 
       var.class=c("ordered.factor","categorical","numeric"), 
       ordered.factor.levels.list=list(Sex=c("M","F")),bep="BEP")
)

10.5 Fit separate models for each variable instead of fitting an additive model

kable(
CoxTab(data=sample.data,tte="OS", cens="OS.event",  var=c('Sex',"Country","Age"),
       additive=FALSE)
)

11.1 Contunuous outcome

SummaryTwoGroups(outcome.var = input$OS, treatment.var = input$Arm, 
            placebo.code = "CTRL", active.code = "TRT", 
            outcome.class = "continuous")

##  Effect.Size        Lower        Upper            P Mean.Placebo 
##   0.79113129   0.19464513   1.38761744   0.00942898   5.77854999 
##  Mean.Active 
##   6.56968128

SummaryTwoGroups(outcome.var = input$OS, treatment.var = input$Arm, 
            placebo.code = "CTRL", active.code = "TRT", 
            outcome.class = "continuous", covariate.var = input$Sex)

##  Effect.Size        Lower        Upper            P Mean.Placebo 
##  0.795751256  0.199865806  1.391636707  0.008954882  5.563721382 
##  Mean.Active 
##  6.359472638

SummaryTwoGroups(outcome.var = input$OS, treatment.var = input$Arm, 
            placebo.code = "CTRL", active.code = "TRT", 
            outcome.class = "continuous", covariate.var = input$Sex, 
            return.fit = TRUE)

## [[1]]
##  Effect.Size        Lower        Upper            P Mean.Placebo 
##  0.795751256  0.199865806  1.391636707  0.008954882  5.563721382 
##  Mean.Active 
##  6.359472638 
## 
## [[2]]
## 
## Call:
## lm(formula = outcome.var[subgroup.var] ~ covariate.var[subgroup.var] + 
##     treatment.var[subgroup.var])
## 
## Coefficients:
##                    (Intercept)    covariate.var[subgroup.var]M  
##                         5.5637                          0.4204  
## treatment.var[subgroup.var]TRT  
##                         0.7958

11.2 Binary outcome

SummaryTwoGroups(outcome.var = input$OS.event, treatment.var = input$Arm, 
            placebo.code = "CTRL", active.code = "TRT", 
            outcome.class = "binary")

##  Effect.Size        Lower        Upper            P  Rsp.Placebo 
##  -0.04846512  -0.12251455   0.02558430   0.19901328   0.20879121 
##   Rsp.Active    N.Placebo     N.Active nRsp.Placebo  nRsp.Active 
##   0.16032609 182.00000000 368.00000000  38.00000000  59.00000000

11.3 Survival variable

SummaryTwoGroups(outcome.var = input[, c("OS", "OS.event")], 
            treatment.var = input$Arm, placebo.code = "CTRL", 
            active.code = "TRT", outcome.class = "survival")

## CTRL.events      CTRL.n    CTRL.MST  TRT.events       TRT.n     TRT.MST 
##  38.0000000 182.0000000  14.8829569  59.0000000 368.0000000          NA 
## Effect.Size       Lower       Upper           P 
##   0.6667451   0.4432032   1.0030365   0.0517253

SummaryTwoGroups(outcome.var = input[, c("OS", "OS.event")], 
            treatment.var = input$Arm, placebo.code = "CTRL", 
            active.code = "TRT", outcome.class = "survival", 
            covariate.var = input$Sex)

##  CTRL.events       CTRL.n     CTRL.MST   TRT.events        TRT.n 
##  38.00000000 182.00000000  14.88295688  59.00000000 368.00000000 
##      TRT.MST  Effect.Size        Lower        Upper            P 
##           NA   0.63926120   0.42390908   0.96401541   0.03277593

SummaryTwoGroups(outcome.var = input[, c("OS", "OS.event")], 
            treatment.var = input$Arm, placebo.code = "CTRL", 
            active.code = "TRT", outcome.class = "survival", 
            covariate.var = input$Sex, strat.factor.var = input$Age)

##  CTRL.events       CTRL.n     CTRL.MST   TRT.events        TRT.n 
##  38.00000000 182.00000000  14.88295688  59.00000000 368.00000000 
##      TRT.MST  Effect.Size        Lower        Upper            P 
##           NA   0.60116855   0.37830923   0.95531273   0.03128551

13.1 Generate example dataset

example <- data.frame( y=c(rnorm(30)+10, rnorm(4)+20, rnorm(15)+15, NA), 
                            time=c(rep("t2", 30), rep("t4",4), rep("t1", 15), "t3"),
                            grp=sample(1:3, 50, TRUE), sex=sample(1:2, 50, TRUE))
head(example)

##           y time grp sex
## 1  9.180521   t2   1   1
## 2 10.298517   t2   3   2
## 3 11.109431   t2   3   2
## 4 11.324706   t2   2   1
## 5  8.307494   t2   1   1
## 6 10.612536   t2   3   2

str(example)

## 'data.frame':    50 obs. of  4 variables:
##  $ y   : num  9.18 10.3 11.11 11.32 8.31 ...
##  $ time: Factor w/ 4 levels "t1","t2","t3",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ grp : int  1 3 3 2 1 3 2 3 3 3 ...
##  $ sex : int  1 2 2 1 1 2 2 2 1 1 ...

13.2 Specify data as named list

BoxPlot(list(a=rnorm(50,,3), b=rnorm(25,1,4), c=rnorm(75,2,1)))

13.3 Same plot, now horizontally plotted

BoxPlot(list(a=rnorm(50,,3), b=rnorm(25,1,4), c=rnorm(75,2,1)), horizontal=TRUE,
             Xaxis=list(las=2, hadj=2), Xaxis2=list(las=2, hadj=-.25))

13.4 Specify data as numeric matrix

BoxPlot(matrix(rnorm(100), ncol=4, dimnames=list(NULL, LETTERS[1:4])) )

13.5 Specify data as numeric (unnamed) vectors

BoxPlot(rnorm(50,,3), rnorm(25,1,2), rnorm(75,2,1))

13.6 … horizontally plotted (no axis-label rotation done here)

BoxPlot(rnorm(50,,3), rnorm(25,1,2), rnorm(75,2,1), horizontal=TRUE)

13.7 Plot values ‘y’ according to time ‘time’ (factor levels are automatically ordered as e.g. function sort does)

BoxPlot(example, y~time, sc.pch=16)

## Number of samples removed due to missing Y: 1

13.8 Now as box-percentile plot

BoxPlot(example, y~time, sc.pch=16, box.type="bp")

## Number of samples removed due to missing Y: 1

13.9 With custom main title

BoxPlot(example, y~time, sc.pch=16, box.type="bp", Title=list(main="Custom Main Title"))

## Number of samples removed due to missing Y: 1

13.10 The same plot with trend line (connects either means or medians) and Y-axis label

BoxPlot(example, y~time, trend="median", ylab="Y-Axis Label")

## Number of samples removed due to missing Y: 1

13.11 Use an addition grouping variable to color points in the stripchart

BoxPlot(example, y~time, trend="median", ylab="Y-Axis Label",
           sc.col=c("red", "blue", "green")[example$grp] )

## Number of samples removed due to missing Y: 1

13.12 Use yet another grouping factor for plotting symbols in the stripchart

BoxPlot(example, y~time, trend="median", ylab="Y-Axis Label",
           sc.col=c("red", "blue", "green")[example$grp],
             sc.pch=c(5,10)[example$sex] )

## Number of samples removed due to missing Y: 1

legend("bottomright", fill=c("red", "green", "blue", "white", "white"), 
           legend=c("Stage I", "Stage II", "Stage III", "Female", "Male"), 
           pch=c(-1, -1, -1, 5, 10), border=NA)

13.13 Generate new dataset with different stucture

example2 <- data.frame(y1=12+rnorm(15), y2=15+rnorm(15), y3=25+rnorm(15))

13.14 Boxplot with trend lines (mean) and grid added to the plot

BoxPlot(example2, var=c("y1", "y2", "y3"), Grid=TRUE, trend="mean")

13.15 Now as box-percentile plot

BoxPlot(example2, var=c("y1", "y2", "y3"), box.type="bp", Grid=TRUE)

13.16 Use yet another way to specify the data

BoxPlot(rnorm(1856, 5), runif(1245, 2,10), exp(rnorm(2311)), sc.col=as.rgb("black", .05), 
            box.type="bp", Xaxis=list(labels=c("~N(5,1)", "~Unif(2,10)", "~exp(N(0,1))")), ylim=c(0,15))     

13.17 Specifying data as numeric matrix and using a trend-line connecting the means

    mat <- matrix(c(rep(10,45), rep(15,45), rep(35,45))+rnorm(135), ncol=3)
    BoxPlot(mat, trend="mean", trend.col="red", Ylabel=list(text="Example Measurements"))

13.18 Multiple grouping factors can be specified via the formula interface which is exemplified using the mtcars dataset, of interest is miles per gallon (mgp) depending on number of gears and on the number of cylinders

data(mtcars)
BoxPlot(mtcars, mpg~gear:cyl)  

13.19 Now ‘cyl’ is explicitly nested within ‘gear’ which changes the ordering of combined grouping factors (which is identical to using formula ‘mpg~cyl:gear’).

BoxPlot(mtcars, mpg~cyl %in% gear)     

13.20 More meaningful group-labels are best specified using custom factor-level names (increase width of the plot window)

dat <- mtcars
dat$cyl <- factor(dat$cyl, levels=c(4,6,8), labels=c("4Cyl", "6Cyl", "8Cyl"))
dat$gear <- factor(dat$gear, levels=c(3,4,5), labels=c("3Gear", "4Gear", "5Gear")) 
BoxPlot(dat, mpg~cyl %in% gear)

13.21 One can use a table as Xaxis label representing the factor-level combination defining sub-classes

BoxPlot(dat, mpg~cyl:gear, XaxisTab=list(),mar=c(8,3,5,1))

13.22 With the original factor levels and as horizontal plot

BoxPlot(mtcars, mpg~cyl:gear, XaxisTab=list(), mar=c(5,8,5,4), 
             horizontal=TRUE, Ylabel=list(text="Y-axis label now appearing on X-axis"))

13.23 Using smaller bottom margin will result in smaller table height

BoxPlot(dat, mpg~cyl:gear, XaxisTab=list(font=2, col="darkblue", cex=1.25), mar=c(5,3,5,1))

13.24 One can use different font-settings for rownames and cells of the table

BoxPlot(dat, mpg~cyl:gear, XaxisTab=list(Label=list(font=2, col="darkblue", cex=1.25), 
             Text=list(col="red")), mar=c(5,3,5,1))

13.25 Use more crossed factors

BoxPlot(dat, mpg~cyl:gear:vs, XaxisTab=list(), mar=c(5,3,5,1))

13.26 Alternatively one can use the ‘Xaxis’ argument, but the ordering of these labels is not checked which is not important for automatically generated group-labels as shown in the previous example

BoxPlot(mtcars, mpg~cyl %in% gear, 
             Xaxis=list(labels=paste(rep(c("4Cyl", "6Cyl", "8Cyl"),3), 
                        c(rep("3Gear",3), rep("4Gear",3), rep("5Gear",3)), sep=".")))

13.27 The same plot with some fancy options

BoxPlot(dat, mpg~cyl %in% gear, Title=list(main="Miles per Gallon by Number of Gears", 
             col.main="Green", cex.main=2.5), vline=c(3.5, 6.5), vl.lty=2, vl.col="gray", vl.lwd=2,
           Xaxis=list(labels=NA, at=1:9, tick=TRUE), col=c(rep("blue", 3), rep("red", 3), rep("green", 3)),
           Xaxis2=list(tick=FALSE), Yaxis=list(at=seq(10,34,2)), Grid=list(x=1:9, y=seq(10,34,2)),
           Xlabel=list(text=paste(rep(c("4Cyl", "6Cyl", "8Cyl"),3), c(rep("3Gear",3), rep("4Gear",3), rep("5Gear",3))),
                       at=1:9, las=2, adj=1, line=0.75, col=c(rep("blue", 3), rep("red", 3), rep("green", 3))), 
           mean.col=c(rep("cyan", 3), rep("orange", 3), rep("magenta", 3)), Box=FALSE, trend="mean",mar=c(6,4,5,2) )

13.28 Horizontal fancy plot

BoxPlot(mtcars, mpg~cyl %in% gear, Title=list(main="Miles per Gallon by Number of Gears", col.main="#84d52b", cex.main=1.5), 
        vline=c(3.5, 6.5), vl.lty=2, vl.col="gray", vl.lwd=2,
        Xaxis2=list(tick=FALSE, las=2, hadj=-.25), Yaxis=list(at=seq(10,34,2)), Grid=list(x=1:9, y=seq(10,34,2)),
        mean.col=c(rep("cyan", 3), rep("orange", 3), rep("magenta", 3)), Box=FALSE, trend="mean",
        mar=c(3, 7, 4, 4), horizontal=TRUE, sc.pch=c(0, 15)[dat$am+1], sc.col="wheat4",
        XaxisTab=list(Text=list(col=c(rep("cyan", 3), rep("orange", 3), rep("magenta", 3)))) )
 
 legend(x="topright", pch=c(0, 15), legend=c("automatic", "manual"), box.lty=0, col="wheat4")

14.1 Biomarker Endpoint Progression

PlotLong(longbmkr, aes(x=vm, y=ep),
         xlab = 'Visitation Month',
         ylab = 'Biomarker Endpoint',
         labs.title = 'Mean ± SEM')

PlotLong(longbmkr, aes(x=vm, y=ep), fun.data = 'tukey',
         xlab = 'Visitation Month',
         ylab = 'Biomarker Endpoint',
         labs.title = 'Tukey Whiskers and Hinges')

14.2 Plotting Subpopulations

PlotLong(longbmkr, aes(x=vm, y=ep, group=trt, color=trt, fill=trt), 
         fun.data = 'tukey', facet.fun = . ~ sex,
         xlab = 'Visitation Month',
         ylab = 'Biomarker Endpoint',
         labs.title = 'Biomarker Timecourse',
         labs.caption = 'Ribbons represent Tukey hinges and whiskers')

14.3 Including sample counts

library(dplyr)
PlotLong(longbmkr %>% filter(vm <= 24), 
         aes(x=vm, y=ep, group=trt, color=trt, fill=trt), 
         fun.data = 'tukey', facet.fun = . ~ sex,
         show.counts = 'table',
         xlab = 'Visitation Month',
         ylab = 'Biomarker Endpoint',
         labs.title = 'Biomarker Timecourse',
         labs.caption = 'Ribbons represent Tukey hinges and whiskers')

14.4 Plotting Deviations as Errorbars

PlotLong(longbmkr %>% filter(vm <= 24), 
         aes(x=vm, y=ep, group=trt, color=trt, fill=trt), 
         fun.data = 'tukey', facet.fun = . ~ sex,
         show.counts = 'table',
         plot.style = 'errorbars',
         xlab = 'Visitation Month',
         ylab = 'Biomarker Endpoint',
         labs.title = 'Biomarker Timecourse',
         labs.caption = 'Ribbons represent Tukey hinges and whiskers')